基于动态模态分解的弹道目标平动补偿与微动特征提取方法

doi:10.12305/j.issn.1001-506X.2025.02.12

系统工程与电子技术 ›› 2025, Vol. 47 ›› Issue (2): 451-462.doi: 10.12305/j.issn.1001-506X.2025.02.12

• 传感器与信号处理 • 上一篇

基于动态模态分解的弹道目标平动补偿与微动特征提取方法

李开明¹^,²^,*, 代肖楠¹^,³, 张袁鹏⁴, 姚佳文⁵, 罗迎¹^,²

1. 空军工程大学信息与导航学院, 陕西西安 710077
2. 空军工程大学信息感知技术协同创新中心, 陕西西安 710077
3. 中国人民解放军95806部队, 北京 100076
4. 空军预警学院一系, 湖北武汉 430019
5. 北京遥测技术研究所, 北京 100094

收稿日期:2023-12-06 出版日期:2025-02-25 发布日期:2025-03-18
通讯作者: 李开明
作者简介:李开明 (1982—), 男, 副教授, 硕士研究生导师, 博士, 主要研究方向为雷达成像与目标识别
代肖楠 (1991—), 男, 助理工程师, 硕士, 主要研究方向为雷达成像与目标识别
张袁鹏 (1984—), 男, 讲师, 博士, 主要研究方向为雷达信号处理、雷达目标识别
姚佳文 (1995—), 女, 助理工程师, 硕士, 主要研究方向为雷达信号处理
罗迎 (1984—), 男, 教授, 博士研究生导师, 博士, 主要研究方向为雷达成像与目标识别
基金资助:
国家自然科学基金面上项目(62371468);国家自然科学基金面上项目(62131020);国家自然科学基金面上项目(62301599);国家自然科学基金面上项目(62271500)

Translational compensation and micro-motion feature extraction method of ballistic targets based on dynamic mode decomposition

Kaiming LI¹^,²^,*, Xiaonan DAI¹^,³, Yuanpeng ZHANG⁴, Jiawen YAO⁵, Ying LUO¹^,²

1. College of Information and Navigation, Air Force Engineering University, Xi'an 710077, China
2. Collaborative Innovation Center of Information Sensing and Understanding, Air Force Engineering University, Xi'an 710077, China
3. Unit 95806 of the PLA, Beijing 100076, China
4. The First Department, Air Force Early Warning Academy, Wuhan 430019, China
5. Beijing Research Institute of Telemetry, Beijing 100094, China

Received:2023-12-06 Online:2025-02-25 Published:2025-03-18
Contact: Kaiming LI

摘要/Abstract

摘要：

针对弹道目标平动导致微动特征难以准确提取的问题, 提出一种基于动态模态分解(dynamic mode decomposition, DMD)的弹道目标平动补偿与微动特征提取方法。首先, 在弹道目标微动回波建模的基础上, 对目标的慢时间-距离像序列进行微多普勒(micro-Doppler, m-D)特征曲线分离; 其次, 将分离后的数据向量移位堆叠构建为增广数据矩阵, 并对其进行DMD; 然后, 利用分解后的模态幅值对各模态进行排序, 结合损失函数等信息选取主要模态; 同时, 利用主要模态中的零频率模态完成弹道目标的平动补偿, 从其他主要模态中提取出自旋频率和锥旋频率等微动特征信息; 最后, 对基于DMD的弹道目标平动补偿与微动特征提取方法进行性能分析与对比实验, 验证了所提方法的可行性和稳健性。

关键词: 动态模态分解, 弹道目标, 微多普勒, 平动补偿, 特征提取

Abstract:

To address the problem of difficulty in accurately extracting micro-motion features caused by ballistic target translational motion, a ballistic target translational motion compensation and micro-motion feature extraction method based on dynamic mode decomposition (DMD) is proposed. Firstly, based on the modeling of micro-motion echoes of ballistic targets, the slow-time range profile sequence of the target is subjected to micro Doppler (m-D) feature curve separation. Secondly, the separated data vectors are shifted and stacked to construct an augmented data matrix, and DMD is performed. Then, the decomposed mode amplitudes are used to rank each mode, and the main mode is selected combined with information such as the loss function. The zero frequency mode in the main modes is utilized to achieve translational compensation of ballistic targets, and micro-motion feature information such as spin frequency and cone rotation frequency from other main modes are extracted. Finally, performance analysis and comparative experiments are conducted on the DMD-based ballistic target translational compensation and micro-motion feature extraction methods, verifying the feasibility and robustness of the proposed methods.

Key words: dynamic mode decomposition (DMD), ballistic target, micro-Doppler (m-D), translational compensation, feature extraction

中图分类号:

TN95

李开明, 代肖楠, 张袁鹏, 姚佳文, 罗迎. 基于动态模态分解的弹道目标平动补偿与微动特征提取方法[J]. 系统工程与电子技术, 2025, 47(2): 451-462.

Kaiming LI, Xiaonan DAI, Yuanpeng ZHANG, Jiawen YAO, Ying LUO. Translational compensation and micro-motion feature extraction method of ballistic targets based on dynamic mode decomposition[J]. Systems Engineering and Electronics, 2025, 47(2): 451-462.

图/表 20

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参考文献 31

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参数	参考值
载频/GHz	10
带宽/GHz	1
脉宽/μs	0.5
脉冲重复频率/Hz	600
初始距离R₀/m	593 160
初始速度v/(m/s)	6 050
初始加速度a/(m/s²)	0.5
初始距离估计值R′₀/m	593 159
初始速度估计值v′/(m/s)	6 049
初始欧拉角/rad	(π/3, π/4, π/5)
导弹弹长/m	2
导弹底面半径/m	0.5
自旋频率/Hz	3
锥旋频率/Hz	0.5
仿真回波时长/s	6

前i阶	占比/%
2	81.01
4	91.51
6	99.93
8	99.99

算法	运算时间
EEMD	3.27
VMD	18.05
DMD	6.57

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基于动态模态分解的弹道目标平动补偿与微动特征提取方法

Translational compensation and micro-motion feature extraction method of ballistic targets based on dynamic mode decomposition

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前i阶	占比/%
2	99.99
4	100
6	100
8	100
10	100